COVID-19 Counter Measures Should be Age Specific

Among COVID-19 exposed individuals, people in their 70s have roughly twice the mortality of those in their 60s, 10 times the mortality of those in their 50s, 40 times that of those in their 40s, 100 times that of those in their 30s, 300 times that of those in their 20s, and a mortality that is more than 3000 times higher than for children. Since COVID-19 operates in a highly age specific manner, mandated counter measures must also be age specific. If not, lives will be unnecessarily lost. 

To determine effective public health counter measures against COVID-19, it is important to know the population characteristics of the epidemic [1]. It has been widely reported that mortality rates among those diagnosed and hospitalized are higher in older age groups [2, 3], but to determine public health action, it is the mortality among those exposed or infected that is of primary importance. Absolute risk estimates are uncertain at this stage of the epidemic, due to asymptomatic infected individuals [4] and limited population based testing [1], but with reasonable assumptions about exposure, it is possible to obtain rough estimates of the relative risks in different age groups, as well as upper bounds for the absolute risks.

We consider two alternative exposure scenarios at the early stages of the outbreak in Wuhan, before any social distancing was in place. In Scenario A, the likelihood of being exposed was equal in all age groups. In Scenario B, those <70 had twice the exposure compared to ages 70-79, who in turn had twice the exposure of those 80 and older. The truth probably lies somewhere in between these two scenarios.

Using Wuhan data for the relative risk of a COVID-19 diagnosis after exposure (RRC|E) and national Chinese data for the relative risk of death after a diagnosis (RRD|C) [2], the estimated relative risk of death among those exposed is RR = RRC|E x RRD|C. The Wuhan data better reflect the pre-social distancing phase of the epidemic while the Chinese mortality data increase the sample size of diagnosed individuals, generating more reliable estimates.

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